Data Science Final Project
|
|
- Damian Rich
- 5 years ago
- Views:
Transcription
1 Data Science Final Project Hunter Johns Introduction At its most basic, basketball features two objectives for each team to work towards: score as many times as possible, and limit the opposing team's scoring to the lowest possible number of made shots. Basketball shares this scheme with baseball (albeit with runs instead of field goals), which has undergone a data-driven transformation into an analytical sport; basketball, however, features far more interactions between players than does baseball, and thus has resisted the same kind of data revolution that baseball saw with the work of theoretician Bill James and his executor Billy Bean. Short of developing a sabermetrics for basketball, the purpose of my final project is to see how abstractable the most important interaction in basketball, between the shooter and their defender, is. Since it came out in 2012, I've been a fan of the computer videogame NBA 2k13. As I've played the game, I've become increasingly interested in the way the creators of the game modeled how basketball works, specifically the way the creators abstracted that shooter-defender interaction. As far as I can tell without reading the source code, the probability of a shot being made depends most on the ability of the shooter from the specific place on the floor they are shooting from, the defender's ability in that same place, and how well the defender defends the shooter (though the game does make an effort to introduce player-specific modifiers to replicate real-life player behavior, I'm choosing to disregard this in favor of more basic shooter-defender interaction). Enter my data set: a Kaggle set of of every shot taken by an NBA player in the season, a total of just over 120,000 entries. The set features variables like CLOSE_DEF_DIST, the distance from the shooter to the closest defender, and SHOT_DIST, the distance from the shooter to the basket. Data Wrangling My goal was to develop a classification model using the NBA 2k13 mode of basketball thinking, i.e. shot percentage is based only on a mixture of shooter and defender skill in addition to spatial information like the distance from shooter to basket and shooter to defender. To begin with, I created a new variable, shot_class, which classifies a shot based on distance to the basket. I chose the breaks in shot classification based on where a shot changes characteristics, for instance a shot four feet or closer is most likely a layup (not a shot in the "jump-shot" sense). I could not
2 find a dplyr function which would evaluate cases and assign them a variable value based on the evaluation, so I iterated over the list using a for-loop. for (i in 1:nrow(shot_df)) { if (shot_df$shot_dist[i] < 4) { shot_df$shot_class[i] = "close" } else if (shot_df$shot_dist[i] < 15) { shot_df$shot_class[i] = "mid 1" } else if (shot_df$shot_dist[i] < 22) { shot_df$shot_class[i] = "mid 2" } else if (shot_df$shot_dist[i] > 22) { shot_df$shot_class[i] = "long" } else { shot_df$shot_class[i] = "other" } 4 Ft. and closer - "close" - layup or low post shot 4-15 Ft. - "mid1" - close jump shot, high post shot, or floater Ft. - "mid2" - jump shot inside the 3pt line 22 Ft. and beyond - 3pt shot A problem I then ran into was how to find each player's field goal percentage in each of the zones. For this purpose I created an entirely new data frame for shooters, grouping by the name of the player. I wrangled new variables average shot distance for analysis after the classification model. [See Appendix Chunk #1] In order to find the same data for defenders, I applied a similar method and created a separate data frame for defenders. [See Appendix Chunk #2] In order for a machine-learning blackbox to create a prediction model out of the data frame with all of the season's shots, I needed to put the relevent information (shooter and defender field goal percentages) into each shot case. For instance, if a shot was in the "mid1" classification, or between four and fifteen feet, the code would reference the appropriate case in the defender data frame, access that player's defending field goal percentage for the "mid1" shot area, and apply it to the entry for the shot in question. The code would then do the same for the shooter. [See Appendix Chunk #3] I first gave the shot data frame to the rpart function, but something about the data did not mesh with the function. I then tried the ctree function, which then successfully made a classification model of the data. Using cross-validation, with 100,000 cases as the training set and the
3 remaining 28,069 cases for the training set, the model correctly predicted the result of 62% of shots. [See Appendix Chunk #4] In order to perform analysis of the classification model, I used the model to make a prediction for each case in the set. I then assigned the result of the prediction to a new variable. shot_df <- shot_df %>% mutate(shot_pred = NA) for (i in 1:nrow(shot_df)) { prediction <- predict(shot_pred_tree, shot_df[i,]) shot_df$shot_pred[i] <- prediction } Analysis of Model I started by doing a visual analysis of the discrepancy between the model and the actual result of the shots. (A note: in classification graphs, 1 is a make, 2 is a miss) ggplot(shot_df, aes(x=close_def_dist, y = SHOT_DIST, alpha = 0.1)) + geom_point() + labs(x = "Distance to Closest Defender", y = "Distance to Basket", title = "Classification of Shots, Defensive Distance Vs. Shot Distance") + facet_wrap(~factor(shot_pred)) ggplot(shot_df, aes(x=close_def_dist, y = SHOT_DIST, alpha = 0.1)) + geom_point() + labs(x = "Distance to Closest Defender", y = "Distance to Basket", title = "Result of Shots, Defensive Distance Vs. Shot Distance") + facet_wrap(~factor(shot_result))
4 Looking at the graphs, the model found the same trend towards the bottom of the graph: shots that were closer than about five feet to the basket with a defender separation of more than a few feet were likely to be good. However, the model predicted that shots greater than five feet from the basket with a defender separation of less than about three feet were very unlikely to go in, which in practice did not appear to be true. I did another side-by-side comparison of the model's features, this time of shooter zone FGP versus defender zone FGP. ggplot(shot_df, aes(x=shooter_zone_fgp, y = defender_zone_fgp, alpha = 0.1)) + geom_point() + labs(x = "Shooter Zone FGP", y = "Defender Zone FGP", title = "Classification of Shots, Shooter Zone FGP Vs. Defender Zone FGP") + facet_wrap(~shot_pred) ggplot(shot_df, aes(x=shooter_zone_fgp, y = defender_zone_fgp, alpha = 0.1)) + geom_point() + labs(x = "Shooter Zone FGP", y = "Defender Zone FGP", title = "Result of Shots, Shooter Zone FGP Vs. Defender Zone FGP") + facet_wrap(~shot_result)
5
6 Interestingly, I did not find the same noticable difference in the distribution of actual made versus missed as I found in predicted made versus missed. This is a failure of the model; much more than shot ability goes into a field goal percentage, more even than could be gleaned from this data set. An example of this failure might be a player who is playing out of their most comfortable role on a team. I also faceted by zone to see how results compared with classifications. ggplot(shot_df, aes(x=shooter_zone_fgp, y = defender_zone_fgp, color = factor(shot_pred), alpha = 0.1)) + geom_point() + labs(x = "Shooter Zone FGP", y = "Defender Zone FGP", title = "Classification of Shots, Shooter Zone FGP Vs. Defender Zone FGP") + facet_wrap(~shot_class) ggplot(shot_df, aes(x=shooter_zone_fgp, y = defender_zone_fgp, alpha = 0.1, color = SHOT_RESULT)) + geom_point() + labs(x = "Shooter Zone FGP", y = "Defender Zone FGP", title = "Result of Shots, Shooter Zone FGP Vs. Defender Zone FGP") + facet_wrap(~shot_class)
7 Here it is easier to see the break-down in predictive power in the mid1 and mid2 zones, as well as a decrease in stratification of makes and misses. This suggests that the interaction between shooters and defenders is less pronounced, at least in terms of field goal percentage, for jumpshots inside the three-point arc. Another striking observation is that the makes and misses are stratified horizontally in both data sets, suggesting that the role of the defender in the shooterdefender interaction is not as important as I had thought. To test this, I took out the def_zone_fgp from the prediction model and recieved the same 62% prediciton accuracy. When I removed the CLOSE_DEF_DIST feature, however, the accuracy of the model went down. train_df2 <- shot_df[1:120000,] test_df2 <- shot_df[120001:128069,] shot_pred_tree2 <- ctree(shot_result ~ SHOT_DIST + SHOT_CLOCK + shooter_zone_fgp + shooter_zone_shots + CLOSE_DEF_DIST, data=train_df) plot(shot_pred_tree2) pred_model2 <- predict(shot_pred_tree2, test_df2) conf2 <- table(test_df$shot_result, pred_model2) TP2 <- conf2[1,1] FN2 <- conf2[1,2] FP2 <- conf2[2,1] TN2 <- conf2[2,2] acc2 <- (TP2 + TN2)/(TP2 + TN2 + FN2 + FP2) acc2
8 To get a better idea of the model's accuracy by zone, I made a visualization. pred_by_zone <- shot_df %>% mutate(shot_pred2 = shot_pred - 1) %>% group_by(shot_class) %>% summarize(real_fgp = sum(fgm)/n(), pred_fgp = (1-(sum(shot_pred2)/n()))) ggplot(pred_by_zone %>% arrange(desc(real_fgp)), aes(x = shot_class, y=real_fgp)) + geom_bar(stat="identity") + labs(x = "Shot Class", y = "FGP", title = "Real FGP by Zone") ggplot(pred_by_zone %>% arrange(desc(pred_fgp)), aes(x = shot_class, y=pred_fgp)) + geom_bar(stat="identity") + labs(x = "Shot Class", y = "FGP", title = "Predicted FGP by Zone")
9 According to the data frame and the visualization, the model captures the trend in overall field goal percentages, with a descension by distance. However, the model thinks that close shot are far and away more likely to go in than any other shot, which does not hold up in reality. Conclusion A predicitive model based only on a shooter's basic stats, defender stats, distance to the basket, distance from the defender to the shooter, and shot clock (shots put up at the end of the shot clock tend to be worse than those in the middle of the shot clock) captures some of the trends in shot results, but not all. The abstracted view of basketball, with no player interaction outside of the shooter and their closest defender, does explain some of the data, but a more detailed analysis could be produced with more data on the other eight players on the court.
10 Appendix Chunk #1 off_by_player <- shot_df %>% group_by(player_name) %>% summarize(avgdefdist = mean(close_def_dist), avgshotdist = mean(shot_dist), pts = sum(pts), numshots = n(), pts_to_attempts = pts/numshots, avgdribbles = mean(dribbles)) off_by_player <- off_by_player %>% mutate(close_fgp = NA, close_shots = NA, mid1_fgp = NA, mid1_shots = NA, mid2_fgp = NA, mid2_shots = NA, long_fgp = NA, long_shots = NA, off_sweetspot = NA, off_sweetspot_rad = NA) for (i in 1:nrow(off_by_player)) { close_made <- 0 close_att <- 0 close_def_dist <- 0 mid1_made <- 0 mid1_att <- 0 mid1_def_dist <- 0 mid2_made <- 0 mid2_att <- 0 mid2_def_dist <- 0 long_made <- 0 long_att <- 0 long_def_dist <- 0 sweetspot <- c() counter <- 0 working <- shot_df %>% filter(player_name == off_by_player $player_name[i]) for (j in 1:nrow(working)) { if (working$shot_class[j] =="close") { close_att <- close_att + 1 if (working$shot_result[j] == "made"){ close_made <- close_made + 1 } else if (working$shot_class[j] =="mid 1") { mid1_att <- mid1_att + 1 if (working$shot_result[j] == "made") { mid1_made <- mid1_made + 1
11 } } else if (working$shot_class[j] =="mid 2") { mid2_att <- mid2_att + 1 if (working$shot_result[j] == "made") { mid2_made <- mid2_made + 1 } else if (working$shot_class[j] =="long") { long_att <- long_att + 1 if (working$shot_result[j] == "made") { long_made <- long_made + 1 if (working$shot_result[j] == "made") { counter <- counter + 1 sweetspot <- c(sweetspot, working$shot_dist) off_by_player$close_fgp[i] <- close_made/close_att off_by_player$close_shots[i] <- close_att off_by_player$mid1_fgp[i] <- mid1_made/mid1_att off_by_player$mid1_shots[i] <- mid1_att off_by_player$mid2_fgp[i] <- mid2_made/mid2_att off_by_player$mid2_shots[i] <- mid2_att off_by_player$long_fgp[i] <- long_made/long_att off_by_player$long_shots[i] <- long_att off_by_player$off_sweetspot[i] <- (sum(sweetspot))/counter off_by_player$off_sweetspot_rad[i] <- sd(sweetspot) Chunk #2 def_by_player <- shot_df %>% group_by(closest_defender) %>% summarize(avgdist = mean(close_def_dist), pts_against = sum(pts), numshots = n(), pts_to_attempts = pts_against/numshots) %>% arrange(desc(numshots)) def_by_player <- def_by_player %>% mutate(close_opp_fgp = NA, close_opp_shots = NA, mid1_opp_fgp = NA, mid1_opp_shots = NA, mid2_opp_fgp = NA, mid2_opp_shots = NA, long_opp_fgp = NA, long_opp_shots = NA, def_sweetspot = NA, def_sweetspot_rad = NA)
12 for (i in 1:nrow(def_by_player)) { close_made <- 0 close_att <- 0 mid1_made <- 0 mid1_att <- 0 mid2_made <- 0 mid2_att <- 0 long_made <- 0 long_att <- 0 counter <- 0 working <- shot_df %>% filter(closest_defender == def_by_player $CLOSEST_DEFENDER[i]) for (j in 1:nrow(working)) { if (working$shot_class[j] =="close") { close_att <- close_att + 1 if (working$shot_result[j] == "made"){ close_made <- close_made + 1 } else if (working$shot_class[j] =="mid 1") { mid1_att <- mid1_att + 1 if (working$shot_result[j] == "made") { mid1_made <- mid1_made + 1 } else if (working$shot_class[j] =="mid 2") { mid2_att <- mid2_att + 1 if (working$shot_result[j] == "made") { mid2_made <- mid2_made + 1 } else if (working$shot_class[j] =="long") { long_att <- long_att + 1 if (working$shot_result[j] == "made") { long_made <- long_made + 1 def_by_player$close_opp_fgp[i] <- close_made/close_att def_by_player$close_opp_shots[i] <- close_att
13 } def_by_player$mid1_opp_fgp[i] <- mid1_made/mid1_att def_by_player$mid1_opp_shots[i] <- mid1_att def_by_player$mid2_opp_fgp[i] <- mid2_made/mid2_att def_by_player$mid2_opp_shots[i] <- mid2_att def_by_player$long_opp_fgp[i] <- long_made/long_att def_by_player$long_opp_shots[i] <- long_att Chunk #3 shot_df <- shot_df %>% mutate(defender_sweetspot = NA, shooter_sweetspot = NA, shooter_zone_fgp = NA, shooter_zone_shots = NA, defender_zone_fgp = NA, defender_zone_shots = NA, def_dist_to_sweetspot = abs(shot_dist - (CLOSE_DEF_DIST + defender_sweetspot))) for (i in 1:nrow(shot_df)) { if (shot_df$shot_class[i] == "close") { shot_df$shooter_zone_fgp[i] <- off_by_player$close_fgp[shot_df shot_df$shooter_zone_shots[i] <- off_by_player$close_shots[shot_df shot_df$defender_zone_fgp[i] <- def_by_player $close_opp_fgp[shot_df$closest_defender[i]] shot_df$defender_zone_shots[i] <- def_by_player $close_opp_shots[shot_df$closest_defender[i]] } else if (shot_df$shot_class[i] == "mid 1") { shot_df$shooter_zone_fgp[i] <- off_by_player$mid1_fgp[shot_df shot_df$shooter_zone_shots[i] <- off_by_player$mid1_shots[shot_df shot_df$defender_zone_fgp[i] <- def_by_player$mid1_opp_fgp[shot_df $CLOSEST_DEFENDER[i]] shot_df$defender_zone_shots[i] <- def_by_player $mid1_opp_shots[shot_df$closest_defender[i]] } else if (shot_df$shot_class[i] == "mid 2") { shot_df$shooter_zone_fgp[i] <- off_by_player$mid2_fgp[shot_df shot_df$shooter_zone_shots[i] <- off_by_player$mid2_shots[shot_df shot_df$defender_zone_fgp[i] <- def_by_player$mid2_opp_fgp[shot_df
14 $CLOSEST_DEFENDER[i]] shot_df$defender_zone_shots[i] <- def_by_player $mid2_opp_shots[shot_df$closest_defender[i]] } else if (shot_df$shot_class[i] == "long") { shot_df$shooter_zone_fgp[i] <- off_by_player$long_fgp[shot_df shot_df$shooter_zone_shots[i] <- off_by_player$long_shots[shot_df shot_df$defender_zone_fgp[i] <- def_by_player$long_opp_fgp[shot_df $CLOSEST_DEFENDER[i]] shot_df$defender_zone_shots[i] <- def_by_player $long_opp_shots[shot_df$closest_defender[i]] } Chunk #4 train_df <- shot_df[1:120000,] test_df <- shot_df[120001:128069,] shot_pred_tree <- ctree(shot_result ~ SHOT_DIST + SHOT_CLOCK + shooter_zone_fgp + shooter_zone_shots + defender_zone_fgp + CLOSE_DEF_DIST, data=train_df) plot(shot_pred_tree) pred_model <- predict(shot_pred_tree, test_df) conf <- table(test_df$shot_result, pred_model) TP <- conf[1,1] FN <- conf[1,2] FP <- conf[2,1] TN <- conf[2,2] acc <- (TP + TN)/(TP + TN + FN + FP) acc
Using Spatio-Temporal Data To Create A Shot Probability Model
Using Spatio-Temporal Data To Create A Shot Probability Model Eli Shayer, Ankit Goyal, Younes Bensouda Mourri June 2, 2016 1 Introduction Basketball is an invasion sport, which means that players move
More informationPREDICTING the outcomes of sporting events
CS 229 FINAL PROJECT, AUTUMN 2014 1 Predicting National Basketball Association Winners Jasper Lin, Logan Short, and Vishnu Sundaresan Abstract We used National Basketball Associations box scores from 1991-1998
More informationPerfects Shooting Drill
Perfects Shooting Drill This is a great drill for players to practice shooting with perfect form and also for coaches to teach and correct shooting form. Players form three lines a couple of feet out from
More informationOur Shining Moment: Hierarchical Clustering to Determine NCAA Tournament Seeding
Trunzo Scholz 1 Dan Trunzo and Libby Scholz MCS 100 June 4, 2016 Our Shining Moment: Hierarchical Clustering to Determine NCAA Tournament Seeding This project tries to correctly predict the NCAA Tournament
More informationA Simple Visualization Tool for NBA Statistics
A Simple Visualization Tool for NBA Statistics Kush Nijhawan, Ian Proulx, and John Reyna Figure 1: How four teams compare to league average from 1996 to 2016 in effective field goal percentage. Introduction
More informationOpleiding Informatica
Opleiding Informatica Determining Good Tactics for a Football Game using Raw Positional Data Davey Verhoef Supervisors: Arno Knobbe Rens Meerhoff BACHELOR THESIS Leiden Institute of Advanced Computer Science
More informationBASKETBALL HISTORY OBJECT OF THE GAME
BASKETBALL HISTORY Basketball was invented in 1891 by Dr. James Naismith, an instructor at the YMCA Training School in Springfield, Massachusetts. Unlike football, baseball and other sports that evolved
More informationA Novel Approach to Predicting the Results of NBA Matches
A Novel Approach to Predicting the Results of NBA Matches Omid Aryan Stanford University aryano@stanford.edu Ali Reza Sharafat Stanford University sharafat@stanford.edu Abstract The current paper presents
More informationGame Rules. Basic Rules: The MIAA/Federation High School Rules are used expect as noted below.
Game Rules Basic Rules: The MIAA/Federation High School Rules are used expect as noted below. Coaches: Only the coach and up to 3 assistants are allowed on the bench. Everyone else must be a player who
More informationThe Rise in Infield Hits
The Rise in Infield Hits Parker Phillips Harry Simon December 10, 2014 Abstract For the project, we looked at infield hits in major league baseball. Our first question was whether or not infield hits have
More informationTrial # # of F.T. Made:
OPEN SPINNER APPLICATION APPS Prob Sim ENTER (Spin Spinner) SET UP SPINNER. TABL (graph) the blank graph disappears & will later become a table. SET (zoom) Change Sections to ENTER. ADV (window) Change
More informationBasketball Study Sheet
Basketball Study Sheet History of Basketball Basketball was invented in Springfield, MA in 1891 by James Naismith. When James first invented the game he used a soccer ball and a peach basket as the hoop
More information3 Seconds Violation in which an offensive player remains within the key for more than 3 seconds at one time.
3 Seconds Violation in which an offensive player remains within the key for more than 3 seconds at one time. 3-Point Play When a player is fouled but completes the basket and is then given the opportunity
More informationTHE PERFECTION DRILL
THE PEFECTION DI 1. The drill begins by your team forming one line along a baseline. The line has two balls in it. The following progression takes place: a. A player dribbles the length of the court and
More informationExamining NBA Crunch Time: The Four Point Problem. Abstract. 1. Introduction
Examining NBA Crunch Time: The Four Point Problem Andrew Burkard Dept. of Computer Science Virginia Tech Blacksburg, VA 2461 aburkard@vt.edu Abstract Late game situations present a number of tough choices
More informationDrills to Start Practice
to Start ractice Table of Contents.. Line Lay-ups. Man eave Scoring Drill. Corner Shooting. Man Transition Drill. Minute Full-Court Shooting Drill 7. ost Drop Drill 8.7 Team Shooting Drill 9.8 Fast Break
More information1999 On-Board Sacramento Regional Transit District Survey
SACOG-00-009 1999 On-Board Sacramento Regional Transit District Survey June 2000 Sacramento Area Council of Governments 1999 On-Board Sacramento Regional Transit District Survey June 2000 Table of Contents
More informationPairwise Comparison Models: A Two-Tiered Approach to Predicting Wins and Losses for NBA Games
Pairwise Comparison Models: A Two-Tiered Approach to Predicting Wins and Losses for NBA Games Tony Liu Introduction The broad aim of this project is to use the Bradley Terry pairwise comparison model as
More information14 Bonus Basketball Drills
1 Table Of Contents All-Star Skills Challenge... 3 Back-to-Back Layups... 5 Blind Minefield... 7 Bullseye Shooting... 9 Dead End... 11 Deep Seal... 13 Exhaustion... 15 Free Throw Rebounding... 17 Opposite
More informationBasketball Rules YMCA OF GREATER HOUSTON
Basketball Rules YMCA OF GREATER HOUSTON Association Basketball Rules Rule 1 The Game Section 1 Definition 1.1.1 Basketball is a game played by two teams consisting of five players each. The purpose of
More informationNBA TEAM SYNERGY RESEARCH REPORT 1
NBA TEAM SYNERGY RESEARCH REPORT 1 NBA Team Synergy and Style of Play Analysis Karrie Lopshire, Michael Avendano, Amy Lee Wang University of California Los Angeles June 3, 2016 NBA TEAM SYNERGY RESEARCH
More informationOFFICIAL BASKETBALL RULES SUMMARY OF CHANGES 2014
OFFICIAL BASKETBALL RULES SUMMARY OF CHANGES 2014 1 No Charge Semi-circle Rule The no-charge semi-circle rule shall be applied when the defensive player has one foot or both feet in contact with the no-charge
More information1994 Playcare TM Playing Cards are a product of Playcare TM 937 Otay Lakes Road, Chula Vista, California Barkley Shut Up and Jam is a trademark
1994 Playcare TM Playing Cards are a product of Playcare TM 937 Otay Lakes Road, Chula Vista, California 91913 Barkley Shut Up and Jam is a trademark and 1994 Accolade, Inc. Atari and Atari Jaguar64 are
More informationMOORPARK BASKETBALL ASSOCIATION RULES AND REGULATIONS
MOORPARK BASKETBALL ASSOCIATION 2016-17 RULES AND REGULATIONS PREAMBLE The purpose of the Moorpark Basketball Association (MBA) is to provide training in the sport of basketball in an atmosphere of good
More informationNational Junior Basketball has adopted the National Federation Rule Book for All-Star Tournament play. The following NJB rules also prevail:
all-star TOURNAMENT RULES SECTION 21- ALL-STAR TOURNAMENT National Junior Basketball has adopted the National Federation Rule Book for All-Star Tournament play. The following NJB rules also prevail: 21.1
More informationBASKETBALL PREDICTION ANALYSIS OF MARCH MADNESS GAMES CHRIS TSENG YIBO WANG
BASKETBALL PREDICTION ANALYSIS OF MARCH MADNESS GAMES CHRIS TSENG YIBO WANG GOAL OF PROJECT The goal is to predict the winners between college men s basketball teams competing in the 2018 (NCAA) s March
More informationOpen Post Offense - Motion Offense, Diagrams, Drills, and Plays
Open Post Offense - Motion Offense, Diagrams, Drills, and Plays The open post offense is a great offense that is used at every level. It has gone by the name of the 5 out offense, the spread offense, and
More informationProject Title: Overtime Rules in Soccer and their Effect on Winning Percentages
Project Title: Overtime Rules in Soccer and their Effect on Winning Percentages Group Members: Elliot Chanen, Lenny Bronner, Daniel Ramos Introduction: We will examine the overtime rules of soccer to evaluate
More informationMotion Offense. Movement creates movement, Intelligent movement creates space, Space affords time, and time ensures accuracy
This article is taken from a presentation by Canadian National Women s Team Head Coach, Allison McNeill. The presentation was given to British Columbia s Girls Centre for Performance. Motion Offense General
More informationOfficial NCAA Basketball Statisticians Manual. Official Basketball Statistics Rules With Approved Rulings and Interpretations
2018-19 Official NCAA Basketball Statisticians Manual Orginal Manuscript Prepared By: David Isaacs, longtime statistician and official scorer. Updated By: Gary K. Johnson, and J.D. Hamilton, Assistant
More informationBasketball data science
Basketball data science University of Brescia, Italy Vienna, April 13, 2018 paola.zuccolotto@unibs.it marica.manisera@unibs.it BDSports, a network of people interested in Sports Analytics http://bodai.unibs.it/bdsports/
More informationGame Like Drills for Pregame Warm Up
Game Like Drills for Pregame Warm Up Table of ontents. v. v Wolf. v Block Finishing. v Veer 4.4 v Attack 4. v and v 5. Handoff v 5. Sideline v 6. Line v 7.4 v Weakside 7.5 Arc v 8.6 Tip 9. v and v 0 reated
More informationUsing New Iterative Methods and Fine Grain Data to Rank College Football Teams. Maggie Wigness Michael Rowell & Chadd Williams Pacific University
Using New Iterative Methods and Fine Grain Data to Rank College Football Teams Maggie Wigness Michael Rowell & Chadd Williams Pacific University History of BCS Bowl Championship Series Ranking since 1998
More information2017 USA Basketball 14U National Tournament FIBA Rule Modifications
2017 USA Basketball 14U National Tournament FIBA Rule Modifications *Games will be played in accordance to 2017 FIBA rules and the modifications listed below. Personnel: - Maximum of four bench personnel:
More informationPredicting the development of the NBA playoffs. How much the regular season tells us about the playoff results.
VRIJE UNIVERSITEIT AMSTERDAM Predicting the development of the NBA playoffs. How much the regular season tells us about the playoff results. Max van Roon 29-10-2012 Vrije Universiteit Amsterdam Faculteit
More informationInformation Visualization in the NBA: The Shot Chart
Information Visualization in the NBA: The Shot Chart Stephen Chu University of California, Berkeley chu.stephen@gmail.com ABSTRACT In this paper I describe a new interactive shot chart prototype to be
More informationThe goal of this tryout is to gather a group of young men who are able to achieve academic success in the classroom as well as physical success on
The goal of this tryout is to gather a group of young men who are able to achieve academic success in the classroom as well as physical success on the basketball court. Middle School Tryouts Monday, October
More informationSection 8 Lay Ups. Bacchus Marsh Basketball Association Coaches Manual
Section 8 Lay Ups 8.1 Multi Angle Lay-ups. 8.2 Slide, Pivot, Lay-up. 8.3 Team Dribble Move to Lay-up. 8.4 Dribble, Lay-up, Board Shot. 8.5 X Lay-ups. 8.6 Giant Killers, Floaters. 8.7 Jump Stop Series.
More informationMEMORANDUM. TO: NCAA Divisions I, II and III Coordinators of Men's Basketball Officials.
MEMORANDUM January 16, 2018 VIA EMAIL TO: NCAA Divisions I, II and III Coordinators of Men's Basketball Officials. FROM: J.D. Collins National Coordinator of Men s Basketball Officiating. Art Hyland NCAA
More informationDrill 8 Tandem Defense
Drill 8 Tandem Defense Intermediate Equipment Basketball Purpose This drill helps players learn the concept of working together to protect the basket and close out a shooter, providing a foundation for
More informationReal Soccer Center Futsal Rules
Real Soccer Center Futsal Rules Revised 11.20.2015 General Rules For all ages, there are 4 field players and a Goalkeeper (GK), 5v5. The minimum number of players required to start or continue a match
More informationLate Game Situations (End of practice note card box)
(End of practice note card box) SCORE TIME ON CLOCK SITUATION Up/down - 2 points 6 seconds Team down gets ball on baseline full court to go. Up/down - 2 points 1 minute Team up shooting 2 free throws.
More informationBuilding an NFL performance metric
Building an NFL performance metric Seonghyun Paik (spaik1@stanford.edu) December 16, 2016 I. Introduction In current pro sports, many statistical methods are applied to evaluate player s performance and
More informationCB2K. College Basketball 2000
College Basketball 2000 The crowd is going crazy. The home team calls a timeout, down 71-70. Players from both teams head to the benches as the coaches contemplate the instructions to be given to their
More informationKAMLOOPS ELEMENTARY SCHOOLS BASKETBALL PROGRAM. Philosophy
KAMLOOPS ELEMENTARY SCHOOLS BASKETBALL PROGRAM Philosophy Program Goals To create an environment where students can have fun. To help students develop skills, learn the rules, and appreciate the game of
More informationWayzata Boys Basketball Workout Book (9-12 th Grade)
Wayzata Boys Basketball Workout Book (9-12 th Grade) Daring To Be Great! Wayzata Boys Basketball Workout Booklet Index Ball Handling Workout #1..1 Ball Handling Workout #2..1 Ball Handling Workout #3..2
More informationThis is a simple "give and go" play to either side of the floor.
Set Plays Play "32" This is a simple "give and go" play to either side of the floor. Setup: #1 is at the point, 2 and 3 are on the wings, 5 and 4 are the post players. 1 starts the play by passing to either
More informationMatt Halper 12/10/14 Stats 50. The Batting Pitcher:
Matt Halper 12/10/14 Stats 50 The Batting Pitcher: A Statistical Analysis based on NL vs. AL Pitchers Batting Statistics in the World Series and the Implications on their Team s Success in the Series Matt
More informationBLOCKOUT INTO TRANSITION (with 12 Second Shot Clock)
Xavier As the season wears on, it is very important to remind your team to stay with its strengths. In our case here at Xavier, one of those strengths is our ability to attack in transition off of a missed
More informationSpring/Summer Session
Spring/Summer Session Development Path U12+ C1 U12 + aged teams U11 Soccer The real game U9/10 Academy United In Development Recreational Content Sessions Structure of training 4 technical to one technical
More informationAppendix A continued A: Table Of Lessons
From The Basketball Coachʼs Bible, 2nd Ed Appendix A continued A: Table Of Lessons Table Explanation All table features are discussed in more detail in other sections and are also part of each lesson.
More informationAnkeny Centennial Core Drills
on hange vs. on x x x x In this transition drill the offense will continuously run their offense until the coach blows whistle or yells "change." When "change" is yelled the ball is set down on the floor
More informationEastview Boys Basketball Workout Book
Eastview Boys Basketball Workout Book Toughness! Eastview Boys Basketball Workout Booklet Index Ball Handling Workout #1..1 Ball Handling Workout #2..1 Ball Handling Workout #3..2 Post Workout..2 Perimeter
More informationRevisiting the Hot Hand Theory with Free Throw Data in a Multivariate Framework
Calhoun: The NPS Institutional Archive DSpace Repository Faculty and Researchers Faculty and Researchers Collection 2010 Revisiting the Hot Hand Theory with Free Throw Data in a Multivariate Framework
More informationName: Date: Math in Basketball: Take the Challenge Student Handout
Name: Date: Math in Basketball: Take the Challenge Student Handout When NBA player Elton Brand steps to the free throw line, a number of key variables can influence his shot. Your challenge is to use the
More informationPredicting NBA Shots
Predicting NBA Shots Brett Meehan Stanford University https://github.com/brettmeehan/cs229 Final Project bmeehan2@stanford.edu Abstract This paper examines the application of various machine learning algorithms
More informationBASKETBALL HISTORY RULES TERMS
BASKETBALL HISTORY The rules of basketball are designed to produce a very fast-paced, offensive game, making it one of the most technically demanding ball sports. Invented in 1891 by a Canadian, James
More informationKAMLOOPS ELEMENTARY SCHOOL'S BASKETBALL PROGRAM
KAMLOOPS ELEMENTARY SCHOOL'S BASKETBALL PROGRAM Please adhere to the language of the athletic guidelines/contract. Reminder : it is mandatory for all players to review and sign a copy of this document
More informationMachine Learning an American Pastime
Nikhil Bhargava, Andy Fang, Peter Tseng CS 229 Paper Machine Learning an American Pastime I. Introduction Baseball has been a popular American sport that has steadily gained worldwide appreciation in the
More informationBasketball Officials Exam For Postseason Tournament Consideration
2016-17 Basketball Officials Exam For Postseason Tournament Consideration 1. A1 scores on a lay-up. After the ball has passed through the basket but before TEAM B has secured the ball for the ensuing throw-in,
More informationEvaluating and Classifying NBA Free Agents
Evaluating and Classifying NBA Free Agents Shanwei Yan In this project, I applied machine learning techniques to perform multiclass classification on free agents by using game statistics, which is useful
More informationRosemount Girls Basketball Workout Book
Rosemount Girls Basketball Workout Book Pride, Integrity and Discipline! All Workouts Created by Bryan Schnettler Head Boys Basketball Coach Rosemount High School Rosemount Girls Basketball Workout Booklet
More informationAnthony Goyne - Ferntree Gully Falcons
Anthony Goyne - Ferntree Gully Falcons www.basketballforcoaches.com 1 Kids Shooting Workout #1 I thought I was done after practice. The USA guys taught me that after practice I need to work on my game.
More informationAbstract. 1 Introduction
How to Get an Open Shot : Analyzing Team Movement in Basketball using Tracking Data Patrick Lucey, Alina Bialkowski, Peter arr, Yisong Yue and Iain Matthews Disney Research, Pittsburgh, PA, USA, 15213
More information5-Out Motion Offense Domestic Coaching Guide
5-Out Motion Offense Domestic Coaching Guide The following is an excerpt from Basketball For Coaches the original document can be found here The 5-out motion offense is a fantastic primary offense for
More informationMEMORANDUM. I would like to highlight the two areas where I believe we need additional focus:
MEMORANDUM February 7, 2018 VIA EMAIL TO: NCAA Divisions I, II and III Coordinators of Men's Basketball Officials. FROM: J.D. Collins National Coordinator of Men s Basketball Officiating. Art Hyland NCAA
More informationSPUD Shooters. 7,000 & 10,000 Shooting Club. Great shooters are made, not born
SPUD Shooters 7,000 & 10,000 Shooting Club Great shooters are made, not born How good do you want to be? Being a great, consistent outside shooter can separate you as an individual and us as a team from
More informationSharp Shooting: Improving Basketball Shooting Form
Sharp Shooting: Improving Basketball Shooting Form Research on existing motion data collection software has been collected, and different software will be tested throughout this project. Materials, including
More informationAnthony Goyne - Ferntree Gully Falcons
Anthony Goyne - Ferntree Gully Falcons www.basketballforcoaches.com 1 5 Out Motion Offense Complete Coaching Guide The 5 out motion offense is a fantastic primary offense for basketball teams at any level,
More information2014 Americas Team Camp Coaching Clinic
Notes provided by Jon Giesbrecht - Winnipeg, MB, Canada - CoachGiesbrecht@gmail.com 2014 Americas Team Camp Coaching Clinic Defense with Brett Gunning (Orlando Magic) 4 Characteristics of Great Defensive
More informationGainesville Basketball Association
LEAGUE RULES (Updated 2017-2018) Division Boys 3 nd Grade Regular Virginia High School rules apply unless overridden by the "LEAGUE" rules 1. BALL SIZE Junior Size 27 ball. 2. GAME LENGTH 5 minute stopped
More informationOFSAA FIBA (HIGH SCHOOL)
OFSAA FIBA (HIGH SCHOOL) 2016 F. Cecchetto 2016 INTERVALS OF PLAY An Interval of Play. BEGINS: when the officials arrive on the floor prior to the start of the game, but is not greater than 20 minutes
More informationPractice 12 of 12 MVP LEVEL. Values TIME MANAGEMENT Help the players understand how to manage, school, fun, sports, and other hobbies.
THEME ACTIVITY DETAILS PERCENTAGE OF TOTAL PRACTICE TIME Values TIME MANAGEMENT Help the players understand how to manage, school, fun, sports, and other hobbies. 5% Warm-Up DYNAMIC WARM-UP (1 x each from
More informationNORTH METRO YOUTH BASKETBALL LEAGUE
NORTH METRO YOUTH BASKETBALL LEAGUE A Twin-Cities North Metro Youth Recreational Basketball League The North Metro Youth Basketball League is a group of volunteers from the surrounding communities which
More informationHow to Win in the NBA Playoffs: A Statistical Analysis
How to Win in the NBA Playoffs: A Statistical Analysis Michael R. Summers Pepperdine University Professional sports teams are big business. A team s competitive success is just one part of the franchise
More informationScienceDirect. Rebounding strategies in basketball
Available online at www.sciencedirect.com ScienceDirect Procedia Engineering 72 ( 2014 ) 823 828 The 2014 conference of the International Sports Engineering Association Rebounding strategies in basketball
More information1st - 2nd Grade BASKETBALL RULES
TEAM & COURT 1st - 2nd Grade BASKETBALL RULES 5 v 5 full court 8 ft. basket height The playing court will be the regular boundary lines. GAME TIME Four (4) 8-minute running clock quarters. The clock will
More informationBasic organization of the training field
Burke Athletic Club u4 & 5 Training Scheme Phase I Introduction to Soccer 1v1 Legend The purpose of this training program is to allow the u4 and 5 s the opportunity to learn some basic ideas and experience
More information4 Out 1 In Offense Complete Coaching Guide
4 Out 1 In Offense Complete Coaching Guide October 12, 2016 by Coach Mac The 4 out 1 in offense (also known as 41 ) is one of the most popular and versatile basketball offenses in today s game at all levels.
More informationBilly Beane s Three Fundamental Insights on Baseball and Investing
Billy Beane s Three Fundamental Insights on Baseball and Investing September 10, 2018 by Marianne Brunet How did Billy Beane come up with the moneyball approach to evaluating baseball players? Though Brad
More informationFree Skill Progression Plan. ebasketballcoach.com
1 Free Skill Progression Plan ebasketballcoach.com 2 The tried and true method for running a skill progression is breaking your practice block down into 3 stages: 1. Basic Fundamentals Welcome to ebasketballcoach.com!
More informationUC MERCED INTRAMURAL SPORTS
UC MERCED INTRAMURAL SPORTS A LEAGUE BASKETBALL RULES All rules not covered by this supplement shall be governed by current NCAA basketball rules. RULE 1: COURT AND EQUIPMENT 1.1 Basketballs. A 30 ball
More informationFIELDHOUSE USA BASKETBALL TABLE OF CONTENTS
BASKETBALL RULES FIELDHOUSE USA BASKETBALL TABLE OF CONTENTS 1) Team Rules. 3 i) Coaches ii) Players iii) Rosters iv) Game Roster Forms 2) Game Rules.... 4-6 i) Scorekeepers ii) Forfeits iii) Bench iv)
More informationWorkout #1. "It's not about the number of hours you practice, it's about the number of hours your mind is present during the practice" - Kobe Bryant
Workout #1 "It's not about the number of hours you practice, it's about the number of hours your mind is present during the practice" - Kobe Bryant Drill: Made Shots: Date: Date: Date: Date: Date: Date:
More informationGame Theory (MBA 217) Final Paper. Chow Heavy Industries Ty Chow Kenny Miller Simiso Nzima Scott Winder
Game Theory (MBA 217) Final Paper Chow Heavy Industries Ty Chow Kenny Miller Simiso Nzima Scott Winder Introduction The end of a basketball game is when legends are made or hearts are broken. It is what
More informationWelcome to the ABGC Basketball House League
Welcome to the ABGC Basketball House League This is a program for 1st, 2nd and 3rd graders, all of whom are part of ABGC Development League for new basketball players. The idea is to make the sport as
More informationEAST HANOVER BOYS BASKETBALL ASSOCIATION RULES OF PLAY Version 2.4
2015 2016 RULES OF PLAY VERSION 2.4 EAST HANOVER BOYS BASKETBALL ASSOCIATION 2015-16 RULES OF PLAY Version 2.4 Table of Contents 1) POSSESSION Page 3 2) IN-BOUNDING PASSES. Page 3 3) PERSONAL FOULS. Page
More information2013 Brayden Carr Foundation Coaches Clinic
0 Brayden Carr Foundation Coaches Clinic pg. 0 Brayden Carr Foundation Coaches Clinic Table of Contents. Buzz Williams. Steve Clifford. Seth Greenberg 8. John Lucas 7. Sean Miller 6. Lawrence Frank 6 0
More informationUNITED CHURCH ATHLETIC LEAGUE RULES OF BASKETBALL. Updated 12/2/2016
UNITED CHURCH ATHLETIC LEAGUE RULES OF BASKETBALL Updated 12/2/2016 I. GENERAL RULES A. CONDUCT: Rules of conduct will be specified by those separate rules as enforced by the United Church Athletic League
More informationAn Analysis of NBA Spatio-Temporal Data
An Analysis of NBA Spatio-Temporal Data by Megan Robertson Department of Statistical Science Duke University Date: Approved: Sayan Mukherjee, Supervisor Vikas Bhandawat Scott Schmidler Thesis submitted
More informationTransition. Contents. Transition
Contents 2 on 1 half court 3 on 2-2 on 1 5 on 5 rebound-transition Pistons drill Tommy 3 on 3 shooting drill Transtion shooting with passer 2 2 3 3 5 5 6 1 2 on 1 half court Player triangle 1 marks the
More information1. Unit Objective(s): (What will students know and be able to do as a result of this unit?
Name: N.Bellanco 10 th Grade P.E. Unit: Basketball Duration: From: 11/2/16 To: 11/18/6/16 Period: 6/7 1. Unit Objective(s): (What will students know and be able to do as a result of this unit? (How does
More informationGames, Games, Games By Tim Taggart, Nasco
Games, Games, Games By Tim Taggart, Nasco Plaque Busters Objective: The purpose of this activity is to teach students the proper way to brush their teeth and increase Cardiovascular Endurance. Equipment:
More informationStudent Handout: Summative Activity. Professional Sports
Suggested Time: 2 hours Professional Sports What s important in this lesson: Work carefully through the questions in this culminating activity. These questions have been designed to see what knowledge
More informationPractice Task: Trash Can Basketball
Fourth Grade Mathematics Unit 5 Practice Task: Trash Can Basketball STANDARDS FOR MATHEMATICAL CONTENT MCC4.NF.7 Compare two decimals to hundredths by reasoning about their size. Recognize that comparisons
More informationSTATIC AND DYNAMIC EVALUATION OF THE DRIVER SPEED PERCEPTION AND SELECTION PROCESS
STATIC AND DYNAMIC EVALUATION OF THE DRIVER SPEED PERCEPTION AND SELECTION PROCESS David S. Hurwitz, Michael A. Knodler, Jr. University of Massachusetts Amherst Department of Civil & Environmental Engineering
More informationUW-WHITEWATER INTRAMURAL SPORTS TEAM HANDBALL RULES Last update: January, 2018
UW-WHITEWATER INTRAMURAL SPORTS TEAM HANDBALL RULES Last update: January, 2018 HANDBALL IS A CONTACT SPORT AND INJURIES ARE A POSSIBILITY. THE INTRAMURAL SPORTS PROGRAM ASSUMES NO RESPONSIBILITY FOR INJURIES;
More informationNBA Salary Prediction
NBA Salary Prediction Edbert Puspito Link to codes Imagine You are Lakers GM The team are it worst now, 16-65, last place in Western conference. Kobe will retire, a bunch of player will have their contract
More information